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Universality and Individuality in Neural Dynamics Across Large Populations of Recurrent Networks by Maheswaranathan, Niru; Williams, Alex H.; Golub, Matthew D.; Ganguli, Surya; Sussillo, David is a scholarly article available to read on EtoBox.

What is Universality and Individuality in Neural Dynamics Across Large Populations of Recurrent Networks about?

Task-based modeling with recurrent neural networks (RNNs) has emerged as a popular way to infer the computational function of different brain regions. These models are quantitatively assessed by comparing the low-dimensional neural representations of the model with the brain, for example using canonical correlation analysis (CCA). However, the nature of the detailed neurobiological inferences one can draw from such efforts remains elusive. For example, to what extent does training neural networks to solve common tasks uniquely determine the network dynamics, independent of modeling architectural choices? Or alternatively, are the learned dynamics highly sensitive to different model choices? Knowing the answer to these questions has strong implications for whether and how we should use task-based RNN modeling to understand brain dynamics. To address these foundational questions, we study populations of thousands of networks, with commonly used RNN architectures, trained to solve neuroscientifically motivated tasks and characterize their nonlinear dynamics. We find the geometry of the RNN representations can be highly sensitive to different network architectures, yielding a cautionar

Author

Maheswaranathan, Niru; Williams, Alex H.; Golub, Matthew D.; Ganguli, Surya; Sussillo, David

Published

2019

Language

EN